# The Rise of Open Science: Tracking the Evolution and Perceived Value of Data and Methods Link-Sharing Practices

arXiv: https://arxiv.org/abs/2310.03193 · HTML: https://mlsanity.com/paper/2310.03193/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 4 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [The Semantic Scholar Open Data Platform](https://mlsanity.com/paper/2301.10140/) (2023)
- [Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research](https://mlsanity.com/paper/2112.01716/) (2021)
- [VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups](https://mlsanity.com/paper/2106.00676/) (2021)
- [Did the Model Change? Efficiently Assessing Machine Learning API Shifts](https://mlsanity.com/paper/2107.14203/) (2021)
- [The citation advantage of linking publications to research data](https://mlsanity.com/paper/1907.02565/) (2019)
- [Practices in source code sharing in astrophysics](https://mlsanity.com/paper/1304.6780/) (2013)

## Built on by (most cited first, 3 of 3)

- [Data-driven Discovery with Large Generative Models](https://mlsanity.com/paper/2402.13610/) (2024)
- [Embedding Geometries of Contrastive Language-Image Pre-Training](https://mlsanity.com/paper/2409.13079/) (2024)
- [A Systematic Survey of Natural Language Processing for the Greek Language](https://mlsanity.com/paper/2407.09861/) (2024)

## Abstract (arXiv)

In recent years, funding agencies and journals increasingly advocate for open science practices (e.g. data and method sharing) to improve the transparency, access, and reproducibility of science. However, quantifying these practices at scale has proven difficult. In this work, we leverage a large-scale dataset of 1.1M papers from arXiv that are representative of the fields of physics, math, and computer science to analyze the adoption of data and method link-sharing practices over time and their impact on article reception. To identify links to data and methods, we train a neural text classification model to automatically classify URL types based on contextual mentions in papers. We find evidence that the practice of link-sharing to methods and data is spreading as more papers include such URLs over time. Reproducibility efforts may also be spreading because the same links are being increasingly reused across papers (especially in computer science); and these links are increasingly concentrated within fewer web domains (e.g. Github) over time. Lastly, articles that share data and method links receive increased recognition in terms of citation count, with a stronger effect when the shared links are active (rather than defunct). Together, these findings demonstrate the increased spread and perceived value of data and method sharing practices in open science.